Mads Kærn

6.6k total citations · 2 hit papers
46 papers, 4.7k citations indexed

About

Mads Kærn is a scholar working on Molecular Biology, Genetics and Computer Networks and Communications. According to data from OpenAlex, Mads Kærn has authored 46 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 15 papers in Genetics and 8 papers in Computer Networks and Communications. Recurrent topics in Mads Kærn's work include Gene Regulatory Network Analysis (25 papers), Bioinformatics and Genomic Networks (14 papers) and Evolution and Genetic Dynamics (11 papers). Mads Kærn is often cited by papers focused on Gene Regulatory Network Analysis (25 papers), Bioinformatics and Genomic Networks (14 papers) and Evolution and Genetic Dynamics (11 papers). Mads Kærn collaborates with scholars based in Canada, United States and Denmark. Mads Kærn's co-authors include James J. Collins, William J. Blake, Timothy C. Elston, Charles R. Cantor, Michael Menzinger, Michihiro Araki, Timothy S. Gardner, Hideki Kobayashi, Axel Hunding and Daniel A. Charlebois and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Mads Kærn

45 papers receiving 4.6k citations

Hit Papers

Stochasticity in gene expression: from theories to phenot... 2003 2026 2010 2018 2005 2003 500 1000 1.5k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mads Kærn Canada 22 4.0k 1.4k 425 424 362 46 4.7k
Matthew R. Bennett United States 28 2.6k 0.7× 791 0.6× 370 0.9× 692 1.6× 271 0.7× 69 3.8k
Mukund Thattai India 16 3.7k 0.9× 1.5k 1.1× 387 0.9× 292 0.7× 354 1.0× 40 4.1k
Gábor Balázsi United States 32 3.3k 0.8× 1.1k 0.8× 213 0.5× 234 0.6× 253 0.7× 84 4.1k
Ido Golding United States 32 3.6k 0.9× 1.6k 1.1× 454 1.1× 664 1.6× 649 1.8× 70 5.3k
Abhyudai Singh United States 32 3.8k 1.0× 1.1k 0.8× 299 0.7× 245 0.6× 351 1.0× 221 4.8k
Chikara Furusawa Japan 40 4.1k 1.0× 1.0k 0.7× 212 0.5× 1.0k 2.4× 165 0.5× 162 5.2k
Shmoolik Mangan Israel 6 3.5k 0.9× 952 0.7× 582 1.4× 133 0.3× 196 0.5× 6 4.2k
Ertuğrul M. Özbudak United States 19 2.9k 0.7× 919 0.7× 212 0.5× 176 0.4× 248 0.7× 32 3.3k
José M. G. Vilar Spain 25 1.9k 0.5× 667 0.5× 944 2.2× 306 0.7× 118 0.3× 60 3.1k
William J. Blake United States 13 4.0k 1.0× 1.4k 1.0× 261 0.6× 349 0.8× 352 1.0× 17 4.3k

Countries citing papers authored by Mads Kærn

Since Specialization
Citations

This map shows the geographic impact of Mads Kærn's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mads Kærn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mads Kærn more than expected).

Fields of papers citing papers by Mads Kærn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mads Kærn. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mads Kærn. The network helps show where Mads Kærn may publish in the future.

Co-authorship network of co-authors of Mads Kærn

This figure shows the co-authorship network connecting the top 25 collaborators of Mads Kærn. A scholar is included among the top collaborators of Mads Kærn based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mads Kærn. Mads Kærn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Feng, Victoria, et al.. (2022). The BioExperience Research and Entrepreneurship Challenge: An iGEM-inspired applied research program for BIOSTEM talent and skills development. Frontiers in Bioengineering and Biotechnology. 10. 1046723–1046723. 1 indexed citations
2.
Diep, Patrick, Xingyu Chen, Radhakrishnan Mahadevan, et al.. (2021). Advancing undergraduate synthetic biology education: insights from a Canadian iGEM student perspective. Canadian Journal of Microbiology. 67(10). 749–770. 8 indexed citations
3.
Perkins, Theodore J., et al.. (2019). Human gene expression variability and its dependence on methylation and aging. BMC Genomics. 20(1). 941–941. 20 indexed citations
4.
Camellato, Brendan, et al.. (2018). Measuring Single-Cell Phenotypic Growth Heterogeneity Using a Microfluidic Cell Volume Sensor. Scientific Reports. 8(1). 17809–17809. 9 indexed citations
5.
Flint, Annika, et al.. (2017). NuA4 Lysine Acetyltransferase Complex Contributes to Phospholipid Homeostasis inSaccharomyces cerevisiae. G3 Genes Genomes Genetics. 7(6). 1799–1809. 7 indexed citations
7.
Azizi, Afnan, et al.. (2015). No training required: experimental tests support homology-based DNA assembly as a best practice in synthetic biology. Journal of Biological Engineering. 9(1). 1–1. 7 indexed citations
8.
Charlebois, Daniel A., Gábor Balázsi, & Mads Kærn. (2014). Coherent feedforward transcriptional regulatory motifs enhance drug resistance. Physical Review E. 89(5). 52708–52708. 30 indexed citations
9.
Bœuf, Fabrice Le, Cory Batenchuk, Markus Vähä‐Koskela, et al.. (2013). Model-based rational design of an oncolytic virus with improved therapeutic potential. Nature Communications. 4(1). 1974–1974. 35 indexed citations
10.
Bouchard-Cannon, Pascale, et al.. (2013). The Circadian Molecular Clock Regulates Adult Hippocampal Neurogenesis by Controlling the Timing of Cell-Cycle Entry and Exit. Cell Reports. 5(4). 961–973. 120 indexed citations
11.
Charlebois, Daniel A. & Mads Kærn. (2013). An Accelerated Method for Simulating Population Dynamics. Communications in Computational Physics. 14(2). 461–476. 5 indexed citations
12.
Batenchuk, Cory, et al.. (2011). Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data. PLoS Computational Biology. 7(5). e1002048–e1002048. 11 indexed citations
13.
Batenchuk, Cory, et al.. (2011). Chromosomal Position Effects Are Linked to Sir2-Mediated Variation in Transcriptional Burst Size. Biophysical Journal. 100(10). L56–L58. 22 indexed citations
14.
Charlebois, Daniel A., Nezar Abdennur, & Mads Kærn. (2011). Gene Expression Noise Facilitates Adaptation and Drug Resistance Independently of Mutation. Physical Review Letters. 107(21). 218101–218101. 55 indexed citations
15.
Batenchuk, Cory, et al.. (2010). Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis. BMC Genomics. 11(1). 493–493. 3 indexed citations
16.
Abedi, Vida, et al.. (2010). Estimating the Stochastic Bifurcation Structure of Cellular Networks. PLoS Computational Biology. 6(3). e1000699–e1000699. 27 indexed citations
17.
Kærn, Mads, et al.. (2009). A chance at survival: gene expression noise and phenotypic diversification strategies. Molecular Microbiology. 71(6). 1333–1340. 162 indexed citations
18.
Kuznetsov, Alexey, Mads Kærn, & Nancy Kopell. (2004). Synchrony in a Population of Hysteresis-Based Genetic Oscillators. SIAM Journal on Applied Mathematics. 65(2). 392–425. 90 indexed citations
19.
Kærn, Mads, William J. Blake, & James J. Collins. (2003). The Engineering of Gene Regulatory Networks. Annual Review of Biomedical Engineering. 5(1). 179–206. 150 indexed citations
20.
Kærn, Mads, Michael Menzinger, & Axel Hunding. (2000). A chemical flow system mimics waves of gene expression during segmentation. Biophysical Chemistry. 87(2-3). 121–126. 21 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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